Bridge remaining strength prediction integrated with bayesian network and in situ load testing

Yafei Ma, Lei Wang, Jianren Zhang, Yibing Xiang, Yongming Liu

Research output: Contribution to journalArticle

38 Scopus citations

Abstract

This paper proposes a new framework for predicting remaining bridge strength that integrates a Bayesian network and in situ load testing. It discusses the uncertainty of important factors on corrosion damage and develops a stiffness degradation model for corroded beams based on experimental investigations. Following this, the authors develop a Bayesian network that includes corrosion damage, stiffness degradation, load-deflection response, and other factors to predict structural strength degradation. A numerical example using an existing RC bridge demonstrates the general procedures. The comparison between the theoretical and the experimental deflections from load testing shows that the proposed methodology can efficiently improve prediction accuracy and reduce prediction uncertainty.

Original languageEnglish (US)
Article number04014037
JournalJournal of Bridge Engineering
Volume19
Issue number10
DOIs
StatePublished - Oct 1 2014

Keywords

  • Bayesian network
  • Concrete bridges
  • Corrosion
  • Loads
  • Strength prediction

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction

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